
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "135"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 101 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 97 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	2.70%	0.448711	-0.565448	-0.659276	-1.096591	0.184998	-0.702071	1.403596	1.626829	0.7296	0.7620	0.2923	2.676043	3.032056
2459593	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.575198	-0.150904	0.116213	-1.030420	1.151340	-0.197640	4.701237	3.878478	0.6816	0.6816	0.2892	5.358011	5.562824
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.564719	-0.658621	-0.069266	-0.779606	-0.791514	1.389520	-1.900531	-0.062930	0.0299	0.0354	0.0025	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-1.177571	-0.331238	0.155785	-0.552327	0.117489	-0.774395	0.422821	0.345471	0.6163	0.6406	0.4021	1.606433	1.540759
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	-0.859718	-0.047351	-0.238318	-0.904225	-0.441957	-0.873620	0.675066	1.160826	0.6233	0.6429	0.3943	1.386863	1.329974
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	0.00%	-0.606844	0.875884	-0.165240	-0.868521	1.153884	0.868760	2.155959	2.416932	0.6523	0.6523	0.3249	1.644358	1.688092
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	0.279766	-0.509464	0.189225	-1.035189	0.153583	-0.514761	2.820677	3.110015	0.7242	0.7177	0.2567	2.582487	2.458138
2459587	digital_ok	0.00%	-	-	-	97.90%	2.10%	-0.846790	-0.461060	0.567619	-0.693563	0.556396	-1.018421	0.011223	0.076725	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	17.84%	0.00%	-0.713489	-1.504518	-0.073509	-0.783655	-0.649473	-0.664577	2.118160	0.549133	0.6050	0.6289	0.3929	1.510678	1.526620
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.358180	-0.424319	0.994715	-1.186268	0.083039	-0.341815	-0.635449	-0.492851	0.0356	0.0387	0.0042	0.000000	0.000000
2459584	digital_ok	0.00%	0.00%	0.00%	0.00%	88.02%	11.38%	-0.623462	-0.120192	0.627129	-0.650145	-0.122915	0.037844	1.554792	1.447179	0.5645	0.5909	0.3928	0.000000	0.000000
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	97.31%	0.00%	-0.642846	-0.577451	1.014597	-0.885498	-0.696128	-0.985535	2.396676	1.719852	0.6152	0.6414	0.4159	0.000000	0.000000
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	45.41%	0.54%	-1.096214	-0.952206	0.737725	-0.712348	-0.510416	-1.346544	0.223399	0.530272	0.6181	0.6425	0.4185	1.345223	1.338888
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	96.77%	3.23%	-1.166404	-0.867824	0.768488	-0.848476	-0.680524	-0.870597	2.825836	2.395627	0.6222	0.6481	0.4091	0.000000	0.000000
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	-1.256157	-1.164827	1.246714	-0.771855	-0.392917	-1.117971	0.630527	0.304457	0.6124	0.6388	0.4206	0.753852	0.840115
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.943021	0.252455	-0.225901	-0.486770	-1.088104	-0.447519	0.694391	0.023458	0.0337	0.0321	0.0016	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-0.856598	-0.924394	1.100485	-0.966908	-0.381328	-0.967908	1.344606	2.028988	0.6215	0.6481	0.4116	1.537240	1.419134
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	1.08%	-0.926727	-0.871084	0.940007	-0.675990	-0.384349	-0.960211	2.159826	1.013817	0.6453	0.6708	0.4163	1.556734	1.505553
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	6.45%	20.97%	-0.897549	-0.565801	0.683777	-0.690533	0.366547	-0.706644	1.174964	-0.499861	0.6997	0.7130	0.3660	1.969711	1.821467
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-1.317775	-1.009551	0.700365	-0.774915	0.045963	-0.730909	2.637016	2.249547	0.6423	0.6678	0.4156	1.481666	1.457066
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	-1.122532	-0.970670	1.411216	-0.940569	-0.526616	-0.805852	3.238667	2.275031	0.6316	0.6577	0.4247	1.480119	1.469436
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.54%	-1.298581	-0.872391	0.721123	-0.749827	-0.524872	-1.023725	1.579925	0.729206	0.6447	0.6695	0.4135	1.533289	1.529155
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.726204	0.211898	-0.413090	-0.565969	-1.313386	-0.238068	0.992279	0.357375	0.0322	0.0307	0.0016	nan	nan
2459570	digital_ok	100.00%	29.19%	29.19%	0.00%	100.00%	0.00%	0.626145	1.167465	-1.915615	-1.878179	49.211288	49.034831	0.886701	1.079098	0.4648	0.4897	0.2900	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	2.16%	-1.299558	-0.873990	0.868330	-0.931650	1.922234	-0.265687	1.365802	-0.086087	0.7136	0.7301	0.2721	2.050542	1.905694
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	-1.014245	0.083476	1.164943	-0.813025	-0.738595	-0.875982	2.320638	2.395248	0.6303	0.6535	0.4259	1.570658	1.521335
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	-0.474250	-0.474250	-0.491922	-0.491922	1.366555	1.366555	0.674491	0.674491	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.783713	-0.259517	-1.013007	-0.813131	-0.250878	-0.704424	1.479824	2.054000	0.6439	0.6624	0.4155	1.339794	1.389602
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.654980	0.218339	0.818498	-0.171455	0.096953	0.014599	1.021542	1.463329	0.6389	0.6636	0.4191	1.532447	1.498509
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.594112	0.250329	0.646493	-0.313893	0.326936	0.335072	0.707664	0.327398	0.6496	0.6741	0.4136	1.572942	1.530256
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.384982	0.205128	0.872370	-0.307893	0.488935	0.207667	0.813297	0.657492	0.6394	0.6634	0.4290	1.514698	1.507685
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	-0.597189	0.124481	0.812164	-0.446644	0.198015	-0.127704	1.262516	0.803463	0.6416	0.6655	0.4118	1.594985	1.543068
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-1.347842	-0.166801	0.881815	-0.705560	-0.493268	-1.211813	2.563326	1.251029	0.6409	0.6606	0.4217	1.542025	1.551397
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0288	0.0291	0.0015	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.986269	-0.239906	0.969877	-0.773395	-0.513658	-0.677129	1.750142	1.009023	0.6411	0.6633	0.4152	1.516916	1.494024
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6501	0.6717	0.4248	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6466	0.6686	0.4236	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.784445	9.470901	1.473178	-0.100026	0.076771	0.871832	2.836574	1.637159	0.6530	0.6576	0.4117	6.661383	5.587543
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.634022	3.827462	0.858237	-0.534146	0.286484	-0.685929	1.162974	0.967872	0.6451	0.6535	0.4085	1.594387	1.545708
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0467	0.0467	0.0017	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.083312	6.252626	1.175460	-0.467178	-0.399302	0.607588	2.137959	1.095475	0.6330	0.6440	0.4245	2.816648	2.855208
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459541	digital_ok	100.00%	3.58%	3.58%	0.00%	100.00%	0.00%	-1.012013	6.187711	0.155811	-0.377031	18.366312	11.120888	8.934612	4.992843	0.6744	0.6732	0.3532	4.461357	3.831794
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.684995	-0.571954	0.572266	-0.296001	-0.341271	-0.802430	0.999366	0.507928	0.7117	0.7209	0.3693	1.531976	1.453887
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-1.069905	-1.039857	2.386000	-0.619482	1.057997	-0.216832	6.105666	1.351528	0.6661	0.6860	0.4573	3.334326	3.561345
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.363239	-0.614932	0.672626	-0.248491	0.191444	-0.443849	2.150141	0.233864	0.7493	0.7482	0.3864	1.664757	1.512171
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.511630	-0.698518	0.707896	-0.261073	0.269554	-0.222564	1.516573	0.618987	0.7419	0.7398	0.3839	1.532338	1.498800
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	2.17%	-0.781789	-1.051794	0.868270	-0.857771	-0.146753	-0.565460	1.432484	-0.084645	0.6712	0.6963	0.4291	1.681348	1.521092
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	4.44%	-0.861926	-1.003651	1.518085	-0.914185	-0.158133	-0.817334	1.017457	-0.442285	0.6724	0.6959	0.4279	1.665456	1.542384
2459530	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.693197	0.137756	-0.184023	-0.766908	-0.718684	-0.381629	0.529817	-0.433329	0.0351	0.0359	0.0043	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.854606	-0.766954	0.557776	-1.132429	0.527271	-0.099358	5.702391	3.532097	0.8965	0.9450	0.2053	0.000000	0.000000
2459505	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-1.446715	-1.066716	0.871312	-0.860020	2.836259	2.942309	1.967448	0.006045	0.0961	0.1075	0.0061	1.095432	1.091566
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.137006	-0.659847	1.442689	-0.663671	1.789921	-1.050450	1.319190	2.883230	0.8571	0.8568	0.4731	0.000000	0.000000
2459501	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.544040	-0.662896	0.506208	-0.574514	-0.637227	-0.773777	0.110587	-0.257816	0.1525	0.1670	0.0342	0.000000	0.000000
2459500	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.069714	-0.844319	0.366669	-1.110741	1.384349	-1.114743	-0.485036	-0.459617	0.1548	0.1721	0.0351	0.000000	0.000000
2459499	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.233963	-0.749688	0.894812	-1.190917	1.235491	-0.273773	0.913796	4.860741	0.1579	0.1254	0.0185	0.000000	0.000000
2459498	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.132395	-0.839802	0.678061	-0.882150	1.140376	-1.015568	0.690321	-0.435768	0.1558	0.1613	0.0412	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.255430	-0.844009	0.438094	-0.609570	0.755199	-0.987287	0.509132	0.036866	0.1714	0.1919	0.0404	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.662974	-1.279271	0.956350	-0.738871	1.183458	-1.206121	0.235605	0.553789	0.1941	0.2000	0.0520	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.414362	-1.072549	0.874602	-0.695973	1.127132	-1.029880	-0.158638	0.301949	0.1910	0.2051	0.0573	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.785389	-0.991383	0.935153	-1.123041	0.752147	-0.969348	0.193991	-0.798477	0.1805	0.1975	0.0545	0.000000	0.000000
2459492	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.933855	-0.973000	0.198832	-0.595167	-0.014253	-0.902864	0.911391	-0.686437	0.1562	0.1446	0.0413	0.000000	0.000000
2459491	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.411283	-0.704595	0.530138	-0.845247	0.894723	-0.312202	2.475672	0.453405	0.1675	0.1507	0.0345	0.000000	0.000000
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.740199	-0.829556	0.324518	-0.631867	1.071848	-0.938618	2.579232	0.242486	0.6987	0.7754	0.3953	0.000000	0.000000
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.458613	-0.930597	0.324266	-0.809974	0.530336	-0.671310	3.371781	1.912529	0.7190	0.7836	0.3437	0.000000	0.000000
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	14.29%	-0.680871	-0.543429	0.499707	-0.570782	0.451874	-0.146879	3.221005	0.337982	0.7195	0.7867	0.3710	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 135 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 135, Node 12:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 135: 2459625

Antenna 135: 2459624

Antenna 135: 2459623

Antenna 135: 2459622

Antenna 135: 2459621

Antenna 135: 2459620

Antenna 135: 2459619

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Antenna 135: 2459617

Antenna 135: 2459616

Antenna 135: 2459615

Antenna 135: 2459614

Antenna 135: 2459613

Antenna 135: 2459612

Antenna 135: 2459611

Antenna 135: 2459610

Antenna 135: 2459609

Antenna 135: 2459608

Antenna 135: 2459607

Antenna 135: 2459605

Antenna 135: 2459604

Antenna 135: 2459603

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Antenna 135: 2459598

Antenna 135: 2459597

Antenna 135: 2459596

Antenna 135: 2459595

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Antenna 135: 2459593

Antenna 135: 2459592

Antenna 135: 2459591

Antenna 135: 2459590

Antenna 135: 2459589

Antenna 135: 2459588

Antenna 135: 2459587

Antenna 135: 2459586

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Antenna 135: 2459584

Antenna 135: 2459583

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Antenna 135: 2459580

Antenna 135: 2459579

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Antenna 135: 2459577

Antenna 135: 2459576

Antenna 135: 2459575

Antenna 135: 2459574

Antenna 135: 2459573

Antenna 135: 2459572

Antenna 135: 2459571

Antenna 135: 2459570

Antenna 135: 2459569

Antenna 135: 2459566

Antenna 135: 2459565

Antenna 135: 2459564

Antenna 135: 2459563

Antenna 135: 2459562

Antenna 135: 2459561

Antenna 135: 2459560

Antenna 135: 2459559

Antenna 135: 2459558

Antenna 135: 2459556

Antenna 135: 2459552

Antenna 135: 2459551

Antenna 135: 2459549

Antenna 135: 2459542

Antenna 135: 2459541

Antenna 135: 2459540

Antenna 135: 2459536

Antenna 135: 2459535

Antenna 135: 2459534

Antenna 135: 2459533

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶